scholarly journals Grammatical analysis in the OVIS spoken-dialogue system

1997 ◽  
Author(s):  
Mark-Jan Nederhof ◽  
Gosse Bouma ◽  
Rob Koeling ◽  
Gertjan van Noord
Keyword(s):  
Dialogue System ◽  
Spoken Dialogue ◽  
Spoken Dialogue System ◽  
Grammatical Analysis

scholarly journals Robust grammatical analysis for spoken dialogue systems

1999 ◽  
Vol 5 (1) ◽  
pp. 45-93 ◽  
Author(s):  
GERTJAN VAN NOORD ◽  
GOSSE BOUMA ◽  
ROB KOELING ◽  
MARK-JAN NEDERHOF
Keyword(s):  
Viable Alternative ◽  
Dialogue Systems ◽  
Test Results ◽  
Sources Of Information ◽  
Dialogue System ◽  
Spoken Dialogue Systems ◽  
Spoken Dialogue ◽  
Spoken Dialogue System ◽  
Grammatical Analysis ◽  
Robust Parsing

We argue that grammatical analysis is a viable alternative to concept spotting for processing spoken input in a practical spoken dialogue system. We discuss the structure of the grammar, and a model for robust parsing which combines linguistic sources of information and statistical sources of information. We discuss test results suggesting that grammatical processing allows fast and accurate processing of spoken input.

Crowdsourcing Street-level Geographic Information Using a Spoken Dialogue System

2014 ◽  
Author(s):  
Raveesh Meena ◽  
Johan Boye ◽  
Gabriel Skantze ◽  
Joakim Gustafson
Keyword(s):  
Geographic Information ◽  
Dialogue System ◽  
Spoken Dialogue ◽  
Spoken Dialogue System ◽  
Street Level

scholarly journals Characterizing and Predicting Corrections in Spoken Dialogue Systems

2006 ◽  
Vol 32 (3) ◽  
pp. 417-438 ◽  
Author(s):  
Diane Litman ◽  
Julia Hirschberg ◽  
Marc Swerts
Keyword(s):  
Speech Recognition ◽  
Predictive Power ◽  
Classification Error ◽  
Dialogue Systems ◽  
Dialogue System ◽  
Spoken Dialogue Systems ◽  
Experimental Conditions ◽  
Spoken Dialogue ◽  
Spoken Dialogue System ◽  
Recognition Errors

This article focuses on the analysis and prediction of corrections, defined as turns where a user tries to correct a prior error made by a spoken dialogue system. We describe our labeling procedure of various corrections types and statistical analyses of their features in a corpus collected from a train information spoken dialogue system. We then present results of machine-learning experiments designed to identify user corrections of speech recognition errors. We investigate the predictive power of features automatically computable from the prosody of the turn, the speech recognition process, experimental conditions, and the dialogue history. Our best-performing features reduce classification error from baselines of 25.70–28.99% to 15.72%.

Doing research on a deployed spoken dialogue system: one year of let's go! experience

2006 ◽  
Author(s):  
Antoine Raux ◽  
Dan Bohus ◽  
Brian Langner ◽  
Alan W. Black ◽  
Maxine Eskenazi
Keyword(s):  
Dialogue System ◽  
Spoken Dialogue ◽  
Spoken Dialogue System ◽  
One Year

Interface for barge-in free spoken dialogue system using adaptive sound field control

2004 ◽  
Author(s):  
Tatsunori Asai ◽  
Shigeki Miyabe ◽  
Hiroshi Saruwatari ◽  
Kiyohiro Shikano
Keyword(s):  
Sound Field ◽  
Dialogue System ◽  
Spoken Dialogue ◽  
Spoken Dialogue System ◽  
Field Control

The MonAMI reminder: a spoken dialogue system for face-to-face interaction

2009 ◽  
Author(s):  
Jonas Beskow ◽  
Jens Edlund ◽  
Björn Granström ◽  
Joakim Gustafson ◽  
Gabriel Skantze ◽  
...  
Keyword(s):  
Dialogue System ◽  
Spoken Dialogue ◽  
Spoken Dialogue System ◽  
Face To Face

Evaluation of content presentation strategies for an in-car spoken dialogue system

2006 ◽  
Author(s):  
Heather Pon-Barry ◽  
Fuliang Weng ◽  
Sebastian Varges
Keyword(s):  
Dialogue System ◽  
Spoken Dialogue ◽  
Spoken Dialogue System

scholarly journals Confidence-based adaptivity in response generation for a spoken dialogue system

2001 ◽  
Author(s):  
Kristiina Jokinen ◽  
Graham Wilcock
Keyword(s):  
Dialogue System ◽  
Spoken Dialogue ◽  
Spoken Dialogue System
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